Description
Pandas version checks
- I have checked that this issue has not already been reported.
- I have confirmed this bug exists on the latest version of pandas.
- I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import tempfile
from pathlib import Path
import numpy as np
import pandas as pd
# xlwt is just needed to conveniently create a test-case here
import xlwt
inp = np.r_[:3,np.nan].reshape(2,2)
print(d)
wb = xlwt.Workbook()
ws = wb.add_sheet('Sheet')
for (i,j),v in np.ndenumerate(inp):
ws.write(i,j,v)
with tempfile.TemporaryDirectory() as tdir:
fn = str(Path(tdir)/".xls")
wb.save(fn)
out = pd.read_excel(fn, engine="xlrd")Issue Description
Note that a very similar issue was reported previously: #33853.
However, that one relaed to the openpyxl engine and was thus closed as to be fixed upstream.
It appears that pandas does however vendor xlrd (and there is no issue tracker on the xlrd repo), so I believe this issue belongs here.
pd.read_excel with engine="xlrd" (as required to read xls files) fails when the input document contains infinity values:
...
File ~/.pyenv/versions/3.11.3/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/io/excel/_xlrd.py:109, in XlrdReader.get_sheet_data.<locals>._parse_cell(cell_contents, cell_typ)
105 cell_contents = bool(cell_contents)
106 elif cell_typ == XL_CELL_NUMBER:
107 # GH5394 - Excel 'numbers' are always floats
108 # it's a minimal perf hit and less surprising
--> 109 val = int(cell_contents)
110 if val == cell_contents:
111 cell_contents = val
ValueError: cannot convert float NaN to integer
Expected Behavior
pd.read_excel should succeed.
Installed Versions
INSTALLED VERSIONS
commit : 0f43794
python : 3.11.3.final.0
python-bits : 64
OS : Darwin
OS-release : 22.5.0
Version : Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:20 PDT 2023; root:xnu-8796.121.3~7/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : de_CH.UTF-8
LANG : en_GB.UTF-8
LOCALE : de_CH.UTF-8
pandas : 2.0.3
numpy : 1.23.5
pytz : 2023.3
dateutil : 2.8.2
setuptools : 65.5.0
pip : 23.1.2
Cython : 0.29.34
pytest : 7.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.1.2
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.12.0
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : 0.57.0
numexpr : 2.8.4
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : 2023.4.2
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None